Providing relevance-ordered categories of information

ABSTRACT

A computer-implemented method is disclosed. The method includes receiving from a remote device a search query, generating a plurality of different category-directed result sets for the search query, determining an order for the plurality of category-directed result sets based on the search query, and transmitting the plurality of category-directed result sets to the remote device, in a manner that the result sets are to be displayed in the remote device in the determined order.

CROSS-REFERENCE TO RELATED APPLICATION(S)

This application is a continuation of and claims priority to U.S.application Ser. No. 17/005,713, filed on Aug. 28, 2020, which is acontinuation of and claims priority to U.S. application Ser. No.13/164,550, filed on Jun. 20, 2011, which is a continuation of andclaims priority to U.S. application Ser. No. 11/624,175, filed on Jan.17, 2007 the entire contents of each are hereby incorporated byreference.

TECHNICAL FIELD

Various implementations in this document relate generally to providingrelevance-ordered categories of information to a user.

BACKGROUND

Vast amounts of information are available on the Internet, the WorldWide Web, and on smaller networks. Users of desktop, laptop, andnotebook computers have long enjoyed rich content, like images, audio,video, animation, and other multimedia content from such networks. Asthe number of features available in mobile devices (e.g., cell phones,smartphones, personal digital assistants, personal information managers,etc.) has increased, user expectations for those devices have alsoincreased. Users now expect that much of the rich content will also beavailable from their mobile devices. They expect to have access on theroad, in coffee shops, at home and in the office through mobile devices,to information previously available only from a personal computer thatwas physically connected to an appropriately provisioned network. Theywant news, stock quotes, maps and directions, and weather reports fromtheir cell phones; email from their personal digital assistants (PDAs);up-to-date documents from their smartphones; and timely, accurate searchresults from all their mobile devices.

Because input capabilities may be more limited in a mobile device (e.g.,a smartphone) than in a fixed computing device (e.g., a desktopcomputer), more effort may be required of a user to enter a search query(or other information) from the mobile device than would be required ofthe user in entering the same search query from the fixed computingdevice. In addition, because displays in various mobile devices areoften smaller than displays in fixed computing devices, it may not bepossible to display as much information at any given time in a mobiledevice. Finally, data connections between a mobile device and variousnetworked resources (e.g., the Internet) may be slower thancorresponding data connections between a fixed computing device and thesame networked resources.

SUMMARY

This document describes systems and techniques for providingrelevance-ordered categories of information to a user. In someimplementations, one or more categories of information (e.g., webinformation, image information, news information, navigationalinformation, etc.) are provided to the user in response to a searchquery received from the user, and the first category of information isselected based on a prediction of the category of information the useris likely seeking. In some implementations, the prediction is made usingrules generated by a machine learning system that has been trained usinghistorical search data. In certain circumstances, the prediction may bemade based on statistical correspondence (e.g., developed throughanalysis of other similar queries) between the query received and aspecific category of information. In some implementations, theprediction is made based on a profile corresponding to a user profileassociated with the query, while in others, the prediction is based onaggregated information across multiple users with or without regard todata about a particular user. In some implementations, the prediction ismade based on a combination of factors, including, for example, thestatistical correspondence between the received query and a specificcategory of information and a user profile associated with the query.

In some implementations, a computer-implemented method is disclosed. Themethod comprises receiving from a remote device a search query,generating a plurality of different category-directed result sets forthe search query, determining an order for the plurality ofcategory-directed result sets based on the search query, andtransmitting the plurality of category-directed result sets to theremote device, in a manner that the result sets are to be displayed intile remote device in tile determined order. Tile method may alsoinclude formatting the plurality of category-directed results to bedisplayed in a tabbed array in order of decreasing correlation betweeneach category-directed result set and the search query. Determining theorder may comprise calculating for each category-directed result set alikelihood value that represents a likelihood that a correspondingcategory-directed result set is responsive to the received search query.

In some aspects, determining the order further comprises ordering thecategory-directed result sets based on the calculated likelihood values.In addition, calculating the likelihood value can include retrieving aprofile that is associated with the remote device and that includes adistribution of previously determined correlations between other searchqueries received from the remote device and one or more differentcategories of information; and factoring a portion of the distributioninto the calculated likelihood. In addition, calculating the likelihoodvalue can include retrieving data that is associated with other searchqueries received from other remote devices, the other search queriesbeing substantially similar to the received search query, the dataincluding a distribution of previously determined correlations betweenthe other substantially similar search queries and one or more differentcategories of information; and factoring a portion of the distributioninto the calculated likelihood. The distribution can include multiplesub-distributions, each sub-distribution being related to any one ormore of a classification of device from which the query was received, amodel or model group of device from which the query was received, ageographic area from which the query was received, and an approximatetime of day at which the query was received.

In other aspects, calculating the likelihood value includes retrieving aprofile that is associated with the remote device and performing a firstcalculation to obtain a first result based on a portion of the retrievedprofile, retrieving data that is associated with the search query andperforming a second calculation to obtain a second result based on aportion of the retrieved data, and performing a third calculation basedon a weighted contribution of the first result and the second result.

In certain aspects, the search query of the method can be received froma mobile device. Also, the different categories can include three ormore categories selected from the group consisting of location-basedresults, web results, images, video, shopping, blogs, maps, and books.The method may also include formatting the at least one of the pluralityof category-directed result sets in a web document to be displayed withan array of selectable controls in a graphical user interface on theremote device, with each of the selectable controls corresponding to adifferent category of information. In addition, the method may includegenerating code to display a scroll arrow that, when selected, causesthe array of selectable controls to scroll. Also, the order of theplurality of category-related result sets can be determined based on acorrelation between the search query and aggregated prior user activityrelating to the search query or related search queries, and categoriesfor the result sets.

In another implementation, a system for generating ordered searchresults is disclosed. The system comprises a search query processorconfigured to receive and process a search request from a remote device,a search engine that receives the processed search request and generatesa plurality of category-related search results, and a results rankerthat orders categories containing tile category-related search results.The system may also comprise a profile database that stores profilesassociated with specific remote devices for use by the results ranker inordering the categories. In addition, the system may comprise arelevance filter that stores data about other search queries receivedfrom other remote devices, the data including distributions ofpreviously determined correlations between the other search queries andone or more different categories of information. The results ranker maybe configured to use the data stored in the relevance filter in orderingthe categories.

In some aspects, the results ranker is configured to order categoriesbased on a) a profile associated with the remote device and b) relevancedata correlating other search queries received from other remote devicesand one or more different categories of information, the other searchqueries being substantially similar to the search request. Also, theplurality of category-related search results can include three or morecategories selected from the group consisting of location-based results,web results, images, video, shopping, blogs, maps, and books. The systemmay also include a results formatter that formats at least one of theplurality of category-related search results in a web document to bedisplayed in a graphical user interface on the remote device with anarray of selectable controls, each of the selectable controlscorresponding to a different category of information.

In yet another implementation, a system for generating ordered searchresults is disclosed. The system includes a search query processorconfigured to receive and process a search request from a remote device,a search engine that receives the processed search request and generatesa plurality of category-related search results, and a means for orderingthe category-related search results in categories of search results. Thesystem may also include a relevance filter that stores relevance data,wherein the relevance data includes distributions of previouslydetermined correlations between other search queries received from otherremote devices and one or more different categories of information, theother search queries being substantially similar to the search request.In addition, the system may include a results formatter that formats atleast one of the plurality of category-related search results in a webdocument to be displayed in a graphical user interface on the remotedevice with an array of selectable tabs, each of the selectable tabscorresponding to a different category of information.

The details of one or more implementations are set forth in theaccompanying drawings and the description below. Other features,objects, and advantages will be apparent from the description anddrawings, and from the claims.

DESCRIPTION OF DRAWINGS

FIG. 1 is a block diagram of an example system that can receive queries,generate result sets responsive to the queries and order the resultsets.

FIG. 2 is a block diagram showing additional details of the informationprovider that is shown in FIG. 1 .

FIGS. 3A and 3B are screenshots illustrating various exemplarycategories of information that can be provided in response to a query.

FIGS. 4A-4D are additional screenshots illustrating various exemplarycategories of information that can be provided in response to a query.

FIG. 5 is a flow diagram of an example process, by which a user caninitiate a search query and receive in response ordered categories ofinformation.

FIG. 6 is a screen shot showing an exemplary local one box.

FIG. 7A is a flow chart of a process for identifying the applicabilityof local search results to a query.

FIG. 7B is a flowchart of an example process for preparing a whitelistand blacklist.

FIG. 8 is a block diagram of computing devices that can be used toimplement the systems and methods described herein.

Like reference symbols in the various drawings indicate like elements.

DETAILED DESCRIPTION

The systems and techniques described in this document relate generallyto providing information to an electronic device in a manner that islikely to be relevant to a user of the electronic device. The resultsmay be displayed as search results categorized into a plurality ofresult sets or groups, such as is done with Google search resultscategorized, for example, as web, image, blog, scholar, desktop, maps,news, video, and other sets. Tile set that is initially displayed to auser, and the order of icons that represent the other sets, may beestablished to provide a user with results in positions that arepredicted to be the most helpful to the user. For example, if a userenters “pizza places,” such a search may be determined to be highlycorrelated with “local” search, so that search results associated withthe “local” set of search results are initially displayed to the user,and, for example, “news” results are shown very low in the stack or arenot shown at all (if they are deemed to be irrelevant enough).

In some implementations, the electronic device is a mobile computingdevice, such as a cell phone, smartphone, personal information manager,etc. In particular, for example, systems and techniques are describedfor receiving a query from an electronic device; generating a number ofresult sets that are responsive to the query, where the contents of eachresult set fall into a particular “category” of information (e.g., webcontent, image content, news, maps, etc.); ordering the result setsbased on likelihood that the corresponding category of each result setis most relevant to a user of the electronic device; formatting theordered result sets for presentation in the electronic device; andtransmitting the ordered, formatted result sets to the electronic devicefor display.

The result sets may include, for example, the familiar sets of webcontent, images, book search, video, maps, and local search, which havebeen traditionally displayed as separate search result selectionsgrouped by the particular corpuses of information represented by eachcategory. Those groups may be organized by displaying tabs that aresorted in an order of decreasing calculated relevance to a search queryfor each category. For example, the images-directed group of results maybe determined to be most responsive to a query for “Marilyn Monroe” or“James Dean.” In such a situation, various images may be displayedinitially as a search result, with the remaining groups sorted indecreasing relevance (e.g., shopping, then web, then blogs, then news).In contrast, a scholar or scientific articles-directed group may bedetermined to be most relevant to a query for “polydicyclopentadiene,”and the remaining groups may be sorted accordingly (e.g., web, thenblogs, then news, then shopping, then images). Certain categories may beleft off the results entirely (and thus save, e.g., on transmissionbandwidth and on unnecessary clutter caused by the display of badresults) if they are determined to be sufficiently non-responsive, orthey may be held and only accessed if a user selects a “more” control todisplay the extra, less relevant groups.

Various techniques are described for ordering the result sets. Forexample, result sets may be ordered by a determined correlation betweena particular query (including all of a query or part of a query) and aparticular group, including by aggregated observations of user behaviorin response to receiving query results. For example, it may be observedthat most users who query on “Marilyn Monroe” click an “images” controleven if the initial results are provided as web results. Such userbehavior may indicate that users associate the query closely with imagesand thus prefer to have images displayed first. The correlations betweensearch terms (or, for example, portions of search terms) and searchresults (or portions or other attributes of search results) may becomputed by a machine learning system, as described in more detailbelow.

Also, result sets can be ordered based on a profile associated with theelectronic device or with a user of the electronic device; result setscan be ordered based on a determined or calculated likely relevance tousers of specific classes of electronic devices (e.g., mobile ornon-mobile electronic devices); result sets can be ordered based on acombination of a profile and a determined or calculated relevance; onresult quality; or on other factors.

A more specific example of generating and ordering results in responseto a query is now provided and referenced throughout this document.Consider Joe, a college student and cell phone user, and Jane, astockbroker and smartphone user. Joe may regularly use his cell phone toobtain directions to locations throughout the city in which he residesin order to meet his friends for social engagements. Jane may use hersmartphone throughout the day to check stock prices or news associatedwith various publicly traded companies. Both Joe and Jane may use theirrespective mobile computing devices to enter a particular search query,for example, for “Starbucks,” but each may enter the query for adifferent reason. Joe may want a map to the nearest Starbucks coffeeshop, while Jane may desire to know the price at which Starbucks' commonstock is currently trading. As is described in greater detail below, asystem can provide Joe and Jane with particular categories ofinformation that are most closely correlated with the queries theyenter, as determined from general internet usage across thousands ofusers, so that a term like “Starbucks” is generally associated with“local” search followed by “news” items, and whereas “SBUX” is generallyassociated with the stock ticker symbol, perhaps followed by “news”items. The generated search result groups may be ordered accordingly. Incertain examples, the system may process the same query (e.g.,“Starbucks”) from both Joe's cell phone and Jane's smartphone anddetermine the (possibly different) category of information (e.g.,“local” navigation information, stock price information) that is mostlikely to be relevant to each user.

Advantageously, the system described herein can, in certainimplementations, enhance a user's experience by minimizing the number ofoperations each user must perform in order to obtain the information heor she desires. Time required to retrieve relevant information may beminimized, data traffic may be minimized, and user satisfaction may beincreased. In addition, satisfaction may increase due to the system'ssegmentation of data into logical, understandable groups for the user.For example, certain users may not even think to break results up intosub-groups, and may simply think of themselves searching the entire“web”; the techniques here may provide users with more targeted,corpora-based results.

FIG. 1 is a block diagram of an example system 100 that can receivequeries from various users' electronic devices (e.g., Joe's cell phoneor Jane's smartphone); generate result sets that are responsive to thequeries and that each include a different category of information; orderthe result sets in a manner that is determined to be most likelyrelevant to each user; and transmit the ordered result sets to eachuser. For example, the system 100 can receive a query for “Starbucks”from Jane's smartphone and generate result sets that include informationrelated to “Starbucks” that is categorized as map information, imageinformation, news information, or stock information. In this document, a“query” or “search query” should be understood to include any kind ofdata request that could be fulfilled by multiple categories ofinformation. The system 100 can determine or calculate that the user ofJane's smartphone (e.g., Jane) is most likely to be interested aparticular category of information related to “Starbucks,” can order theresult sets in the appropriate manner, and then transmit the orderedresult sets to Jane's smartphone for display.

In some implementations, multiple result sets, each classifiedcorresponding to its own category of information, are transmitted to theelectronic device that submitted the query (e.g., categories ofinformation that include stock information, map information, newsinformation, image information, etc.), but the result sets can beformatted in a manner that reflects the determined or calculated order.For example, in response to Jane's query, stock information may bepresented first, followed by map information, followed by newsinformation, followed by image information. The information may beplaced in front-to-back order, top-to-bottom order (e.g., withexpandable modules for each category), side-by-side (e.g., withscrollable panels) order, or in another appropriate order. The system100 can receive the same query (e.g., “Starbucks”) from a differentelectronic device (e.g., Joe's cell phone) and calculate or determinethat a user of that electronic device (e.g., Joe) is most likely to beinterested in different categories of information.

In some implementations, tile system 100 generates the same result sets(corresponding to the same categories of information) in response thequery received from Joe's cell phone as were generated in response totile query received from Jane's smartphone, but the result sets areformatted to reflect a different determined or calculated order forpresentation of the various categories of information. For example, inresponse to Joe's query, map information may be presented first,followed by news information, followed by image information, followed bystock information.

To receive and process queries from electronic devices, the examplesystem 100 can include an information provider 103. In someimplementations, the information provider 103 includes a search engine(e.g., a search engine like that provided by Google) that indexesvarious categories of information that are either stored internal to theinformation provider 103 or external to the information provider 103.The search engine can receive a query for information, search itsindexes of different categories of information, and provide a list ofrelevant content that is classified in one or more categories ofinformation. The list of relevant content may include a list ofreferences to tile content, rather than the content itself. Or the listof content may include actual content or previews of the actual contentFor example, a list of relevant news content may include various linksto news content that is stored external to the information provider 103.Each link may be associated with a preview of the actual availableinformation, such as a headline and/or story hook, to help the userdecide whether to follow a particular link and access the actual contentAs another example, a list of relevant image content may include linksto various image sources, along with low-resolution previews orthumbnails of available images to help a user decide whether to follow aparticular link and access the actual image.

In some implementations, actual content that is indexed by theinformation provider 103 is stored in various content providers, such asthe content providers 106 and 109. In some implementations, each contentprovider 106 or 109 stores content belonging to a particular category ofinformation. For example, the content provider 106 might only storeimage information, while the content provider 109 may only store newsinformation. In other implementations, various content providers eachstore and provide multiple categories of information. The contentproviders 106 and 109 may be operated by a single organization or bymultiple organizations.

As shown in FIG. 1 , various networks couple the information provider103, the content providers 106 and 109, and various electronic devices(e.g., a desktop computer 112, a cell phone 115, and a smartphone 118)that can access information provided by the information provider 103 orstored at the content providers 106 or 109. For example, a wide areanetwork (WAN) 121, such as the Internet, can couple the informationprovider 103 and the content providers 106 and 109, and can facilitatedata exchange between the various providers 103, 106 and 109.

Other networks can couple various other devices to each other and to theinformation provider 103 or content providers 106 or 109. For example, awireless network 124 can couple various mobile wireless devices (e.g., acell phone 115 and a smartphone 118) to each other. In someimplementations, the wireless network 124 is coupled directly to the WAN121; in other implementations, the wireless network 124 can be coupledto the wide area network 121 through another network 127, such as thepublic switched telephone network (PSTN). As shown, non-mobile, orfixed, devices such as a desktop computer 112 can also access variousresources of the system 100 through, for example, a connection to widearea network 121 or a connection to the PSTN 127.

An example flow of information in the system 100 is now provided withreference to the scenario above, in which Jane uses her smartphone(e.g., smartphone 118) to query the information provider 103 forinformation related to “Starbucks.” In this example, Jane may enter aquery for “Starbucks” on her smartphone 118, directed to the informationprovider 103 (e.g., Google). When Jane “submits” the query, hersmartphone 118 causes the query to be transmitted to the informationprovider 103 via the wireless network 124 and the wide area network 121,over paths A and B, respectively.

The information provider 103 receives the query and may, in response,generate multiple corresponding internal queries for differentcategories of information. For example, the information provider 103 maygenerate a first corresponding query for images related to Jane's“Starbucks” query; the information provider 103 may generate a secondcorresponding query for stock information related to Jane's “Starbucks”query; other corresponding queries may also be generated for othercategories of information (e.g., web content, news, map information,etc.).

In some implementations, each corresponding query may be processed inconjunction with a different index maintained by the informationprovider 103. For example, the corresponding query for images related to“Starbucks” may be transmitted to an image index 130 via path C₁, and inresponse, the image index 130, in cooperation with the informationprovider 103, may return an image results set via path D₁; similarly,the corresponding query for stock information related to “Starbucks” maybe transmitted to a stock index 133 via path C₂, and in response, thestock index 133, in cooperation with the information provider 103, mayreturn a stock related result set via path D₂. For illustration, thequeries have been shown as being submitted to the indexes, though in amore technical sense, the queries would be submitted to portions of asearch engine that draw upon each of the particular indexes.

Certain result sets may include lists of search results, such as imageor web search results. Other result sets may include a single result.For example, where information about the user's location is known,various “local” results may be generated. In the example, a “Starbucks”query may generate information about the Starbucks that is closest tothe inferred (e.g., via a default location for a user, or the indicationof a town name or zip code in a query) or determined (e.g., via GPScoordinates from the user's device) location of the user. Likewise,weather results may include a single result for a user rather than (orin addition to) a list of various weather-related results. Other,non-local related information may also generate a single-entry set, suchas stock information returned for a “Starbucks” query.

Such single-entry results may be displayed differently than aremultiple-entry results. As an example, certain classes of results (whichmay be referenced as a “one box”) may have specific formats, such as thedisplay of weather by showing a large number representing the currenttemperature and a related graphic (e.g., sun or cloud), along withsmaller displays showing a forecast. The formatting of the result, asopposed to listing results as a simple numbered list, may produce morepleasing and easier-to-understand results that can be viewed in lessscreen space and with less need for the user to navigate throughresults/Such “one box” displays may also be grouped with othercategories of information, e.g., displayed as a tab in an array of tabs,or may be displayed in a different manner.

After obtaining or generating several result sets, the informationprovider 103 can determine an appropriate order for providing the resultsets, format the result sets for display on the device from which theoriginal query was received (e.g., the smartphone 118) and transmit oneor more of the ordered, formatted results to the originating device(e.g., via paths E and F). Certain of the formatting operations may alsobe performed by the originating device (e.g., using JavaScript or otherappropriate mechanisms). Exemplary techniques and methods for orderingthe result sets are described in greater detail below.

In some implementations, in order to maintain the indexes used ingenerating various result sets, the information provider 103automatically gathers and indexes information about available content(e.g., content stored by the content providers 106 and 109). Forexample, an automated information gatherer (e.g., a web crawler orspider-shown in and described in greater detail with reference to FIG. 2) can periodically request and retrieve available content from thecontent provider 106 via paths X₁ and Y₁, respectively, and index thisavailable content in the index 130 (e.g., an index of imageinformation). Similarly, the information provider 103 can periodicallyrequest and retrieve available content from the content provider 109 viapaths X₂ and Y₂, respectively, and index this available content in theindex 133 (e.g., an index of stock information).

The system 100 that is illustrated in FIG. 1 and described above ismerely exemplary. Other similar systems can take other forms. Forexample, various other networks can be employed to couple the devicesand information and content providers shown in FIG. 1 . Each contentprovider 106 and 109 is shown as a single device, but content providerscan include multiple devices that are interconnected by various localand wide area networks. Similarly, the information provider i 03 can bea distributed system that includes tens, hundreds, thousands or moredevices for indexing, storing and providing various categories ofinformation, and the reader will appreciate that the devices that may bepart of the information provider 103 can be networked together invarious ways.

FIG. 2 is a block diagram showing additional exemplary details of theinformation provider 103 that is shown in FIG. 1 . As described above,the information provider 103 can be configured to receive queries fromvarious electronic devices; generate multiple result sets in response toeach received query, where each result set may correspond to a differentcategory of information; order the multiple result sets in a manner thatreflects a likely relevance to each result sets to a user of theelectronic device from which the query was received, format the orderedresult sets for display in the electronic device, and transmit theordered, formatted result sets to the electronic device.

In one implementation, as shown, the information provider 103 employs asearch engine 201 and a number of indexes for indexing or organizingdifferent categories of information. Each index may contain data thatrepresents information that the information provider 103 can provide tousers. For example, the search engine 201 may include a typical Internetsearch engine, and the various indexes can include links to informationstored outside the information provider. The information provider 103can provide links to users (e.g., in response to search queries), andthe actual information corresponding to the links can be provided uponselection of the links by the users.

Some information referenced by entries in the various indexes may bestored within the information provider 103 (e.g., in internal storage202). For example, the internal storage 202 may “mirror” information forwhich search queries are regularly received, such as, for example,breaking news stories, or weather or traffic information. The internalstorage 202 may also store various components needed for generaloperation of the information provider 103, such as applications systemparameters, and information about users who access the system.

In one implementation, as shown, the information provider 103 maymaintain various indexes corresponding to different categories ofinformation. For example, the information provider 103 can include a webindex 204 for indexing web information, an images index 207 for indexingimage information, a news index 210 for indexing news information, a mapindex 213 for indexing maps of various physical locations, anentertainment index 216 for indexing entertainment information, and aweather module 219 for obtaining and organizing weather information. Inother implementations, the information provider 103 may maintain asingle index that indexes all categories of information, including thosecategories depicted by the indexes 204 to 219. The listed categories ofinformation are merely exemplary. Various other categories ofinformation may be available and indexed. Moreover, the indexesthemselves may be arranged differently. For example, one index mayhandle multiple categories of information.

The various indexes (or index) may or may not be cached. For example,the indexes 204-219 may correspond to a separate cached index databaseor databases (not shown) to support faster access to search results. Theindexes 204-219 (or index) may be local to the information provider, orthey may include an external server or storage farm (not shown). Ingeneral, each index may be distributed across many different machinesand many different physical locations. For example, an index may beimplemented by hundreds or thousands of storage devices in multiple datacenters around the globe. The internal storage 202 may also be local ordistributed.

As shown in one implementation, the information provider 103 interactswith the other devices through an interface 222. In someimplementations, the interface 222 includes one or more web servers orapplication servers through which queries are received and from whichinformation responsive to the queries is transmitted. The interface 222is shown as a single interface, but the interface 222 can includevarious other internal interfaces through which information can berouted internal to the information provider. As an example, theinterface 222 may comprise interface devices for a high-speed,high-bandwidth network such as SONET, Infiniband, Ethernet, FastEthernet, Giga-bit ethernet, or any suitable communication hardwareoperating under an appropriate protocol, such that the informationprovider 103 can respond to a large number of distinct requestssimultaneously. The interface 222 may include network interface cards(NICS) or other communication devices and other components or interfacescommon to a high-speed, high-bandwidth network. The precise design ofthe information provider 103 is not critical to this document and cantake any suitable form.

As mentioned above, information in the various indexes 204-219 can begathered by an automated information gatherer 223, such as, for example,a crawler or spider. In some implementations, the automated informationgatherer 223 continuously or almost continuously obtains new informationfrom sources connected to the WAN 121 or from other sources (not shown)connected to the information provider 103. This new information can beprovided to appropriate indexes 204-219 or to the internal storage 202.In addition to being added to various indexes 204-219 or to the internalstorage 202 in an automated fashion, information can be manually loadedin or retrieved from the various indexes 204-219 or from the internalstorage 202 through a maintenance interface 226. In someimplementations, the maintenance interface 226 can allow anadministrator of the information provider 103 to manually add bulk data.

Data requests, such as search queries, can be processed by a requestprocessor 225. The request processor 225 can, in some implementations,parse search queries or other data requests, and if necessary, reformatthem to search strings or search terms that are compatible with thesearch engine 201. For example, in some implementations, the requestprocessor reformats search queries received in HTTP Hypertext or textformat to a format or protocol employed by the search engine 201 Therequest processor 225 may also refine received search queries orrequests for data, by removing articles, prepositions or other termsdeemed to be “non-essential” to completing a search or data access.

In one implementation, as shown, the information provider 103 includes aresponse formatter 228 for formatting information responsive to a searchquery or request for data. In some implementations, the responseformatter 228 formats tile information in a manner that facilitatesdisplay of the information in the specific device from which thecorresponding query was received (e.g., in a format such as HTML, XML(extensible markup language), WML (wireless markup language), or someother suitable format).

When the responsive information includes multiple categories ofinformation, tile response formatter 228 can also order each category ofinformation such that a category of information that is determined orcalculated to be most relevant to tile corresponding query is presentedfirst, or in some more prominent manner than other categories ofinformation that may be less relevant to the original query. Variousexample methods of ordering categories of information are described ingreater detail below, and can include, for example, machine learningapplied to pairs of search terms and search results or search resultcategories, maintaining a profile for users associated with searchqueries, and otherwise maintaining a relevance filter that correlatesspecific search queries or information requests to specific categoriesof information that are most likely to correspond to the specific searchqueries.

To maintain profiles for users who submit search queries, theinformation provider 103 can, in some implementations, include a profilemanager 231 and corresponding profile database 234. In theimplementation shown, the profile database 234 can track variouscategories of information for that particular users frequently search.For example, the profile database 234 may maintain a distribution ofrelevant categories of information searched by a particular user,relative to all search queries received by the user. in particular, withreference to Joe and Jane, the profile database 234 may include aprofile for Joe to track, for example, that 53% of Joe's search queriesare for map information; similarly, the profile database 234 may includea profile for Jane to track, for example, that 61% of Jane's queriesrelate to stock information. In some implementations, a relevant profileis updated each time a new search query is received and/or each time theinformation provider 103 determines the category of information to whicha search query is to be correlated. Multiple profiles may also be keptfor one user, such as a profile wile using a desktop PC (which mayreflect more web search) and a profile while using a desktop mobiledevice (which may reflect more local search).

In some implementations, the profile manager 231 associates a particularprofile with a particular user based on information in the search queryitself (e.g., a user account identifier included with the search query,an electronic device identifier included with the search query, etc.).Upon identifying a particular profile, the profile manager 231 canretrieve corresponding profile information and transmit it to theresponse formatter 228, for example, for use in ordering differentcategories of information that are responsive to the search query.Various methods of determining a category of information (e.g., forupdating profiles) sought by a user are described in greater detailbelow.

To correlate particular queries or parts of queries to particularcategories of information, the information provider 103 can, in someimplementations, include a relevance filter 237 and correspondingrelevance database 240. The relevance may be expressed by a set ofscores or a set of rules or both. For example, a machine learning systemmay be provided with a set of data, such as logs of prior search queries(or other instances of use of a system), and perhaps information aboutreactions to the queries (e.g., whether particular search results wereclicked on, or whether a particular group of results was selected fordisplay after the initial search results were shown). The informationprovided to the system may be labeled, such as indicating the nature ofeach piece of information, e.g., whether it was associated with a“local” search or not. The label may be positive or negative, toindicate, respectively, whether it is part of the class of instances tobe classified, or not

The information may also include features, which can be Boolean (e.g.,indicating that the feature is present or absent) or continuous (e.g.,indicating that the feature has an associated real value). Features maybe represented by strings, or by a fingerprint of a string, representedby a pair in the form of feature_type:value. Feature types may include,for example, queries, urls, and scoring components for ranking of searchresults. Features may be selected so that the number of present featuresper instance is relatively low, such as under 300.

In addition, the information may include prior probability indicators,which are the probabilities that particular instances are positive,without looking at any of the features. One exemplary default value forthe prior probability is the number of positive training instances overa total number of training instances. A log odds of an event can bedetermined from the probability (p) of the event as: ln(p/(1−p)).

From analyzing a set of training data, a system may generate a set ofrules, such as rules for identifying whether search results inparticular groups should be ranked above results in other groups interms of displaying the results in a visually otherwise prominent way toa user. Each rule may include a condition (i.e., combinations offeatures, such as the features for an instance multiplied together). Acondition matches an instance if it has all the features in thecondition. Weights can also be applied to each rule, where a weight(which may be positive or negative) measures a change in the log-oddsthat a label is positive if a condition matches an instance. The systemmay be configured to create a set of rules that together provide theprobability that a given instance has a positive label, by maximizingthe training data without over-fitting it.

In one example relevant here, such a machine learning system may beprovided with training data in the forms of queries, along withindications of the type of corpus or category with which a usercorrelated each query. The various features may include the entire queryor parts of the query, such as single particular terms in the query. Inaddition, the features may include other parameters of the query, suchas the language of the query, the location from which the query wassubmitted (determined, e.g., by a domain associated with the submissionof the query), and a determined quality level of results provided inresponse to the query.

In one implementation, the relevance filter 237 can store each receivedinstance of a particular search query (e.g., a search query for“Starbucks”) and can also or alternatively track a determinedcorrelation between the query and a particular category of information(e.g., web information, image information, news information, mapinformation, etc.). In addition, as noted above, the filter 237 maycontain scoring or rules data to be applied to various inputs such asqueries, in generating an indicator of a predicted relevance forcategories of data returned as search results.

In some implementations, the relevance filter 237 is updated each time anew search query is received by the information provider 103 and againwhen the information provider 103 determines the category of informationto which the search query was directed. Alternatively, batch processingand updating of scoring data or rules may occur. In someimplementations, relevance information corresponding to a specificsearch query may be provided to the response formatter 228 for use inordering categories of information that are responsive to a particularsearch query.

FIGS. 3A and 38 illustrate various example categories of informationthat can be provided in response to a user query. FIG. 3A depictsexemplary information that may be provided in response to a query for“Starbucks.” FIG. 3B depicts exemplary information that may be providedin response to a query for “Steven Spielberg.” For purposes of example,FIGS. 3A and 3B illustrate information that is formatted for a largerdisplay, such as a display that may typically be included in anon-mobile device such as a desktop computer, or a larger mobile device,such as a laptop computer; FIGS. 4A and 48 illustrate information thatis formatted for a smaller display that may be typically included in amobile communications device, such as a smartphone or cell phone. Thereader will appreciate, however, that, unless specifically notedotherwise, the systems and methods described in this document are notlimited to either “mobile” or “non-mobile” devices, nor are they limitedto devices having “larger” displays or to devices having “smaller”displays.

As shown in FIG. 3A, one category of information that may be provided inresponse to a query for “Starbucks” is local location information (e.g.,maps and directions) associated with Starbucks coffee shops near aparticular location (depicted by a screenshot 302). The locationinformation can include a list 305 of physical locations (e.g.,Starbucks coffee shops) corresponding to the query and relative to aspecific geographic area (e.g., Minneapolis, Minn.). As shown in oneimplementation, the street addresses of each location are provided in anaddress pane 308, and an annotated map 311 is provided in a map pane 314that graphically depicts the physical location of each address.

Various controls can be provided to allow a user to manipulate theannotated map 311. For example, zoom controls 317 can be provided toallow a user to zoom in on map information associated with a particularlocation, and pan controls 320 can be provided to allow a user to adjustthe center of the map by scrolling the map to one side or another, ortowards the top or bottom. Other controls and features can be provided.For example, in some implementations, user selection of an annotation onthe map (e.g., by clicking the location with a mouse or other pointingdevice, or by selecting a button on a telephone keypad corresponding toa search result icon) can cause the location information to behighlighted or additional location information to be provided (e.g., atelephone number associated with the physical location, nearby streetintersections, store hours, etc.).

Another category of information that may be provided in response to aquery tor “Starbucks” is image information, as is depicted by ascreenshot 323. As shown in one implementation, the image informationcan include images of products offered by Starbucks coffee shops (e.g.,particular coffee drinks 326, or a particular coffee mug 329), or imagesof particular coffee shops (e.g., a storefront 332). In someimplementations, additional information can be provided upon selectionof an image (e.g., by a user clicking on an image with a mouse or otherpointing device), such as information about the source of the image, ora link to a web site that provides the image or a product associatedwith the image.

Another category of information that may be provided in response to aquery for “Starbucks” is news information, as is depicted by ascreenshot 335. In one implementation, the news information can includenews releases issued by Starbucks itself, or news articles by variousnews agencies or news providers about Starbucks. In someimplementations, news articles are sorted by quality of result, asindicated, for example, by recency of an article, the quality of thesite producing the article, and other factors.

Various other categories of information may also be available inresponse to a query for “Starbucks.” For example, stock information (notshown) may be available, and may include a current stock price, tradingvolume, an indication of whether the stock price has recently increasedor decreased, information about directors or executives associated withthe corresponding corporation, etc. As another example, other general“web” information (not shown) may be available in response to a queryfor “Starbucks” and may include links to various websites, blogs orarticles that discuss, for example, various aspects of Starbucks' coffeeproducts, Starbucks' corporate policies, specific Starbucks' stores,music or concerts offered at specific Starbucks' locations, etc. Asother examples, other information may include video information,shopping information, electronic books or periodicals, etc.

In addition, where the intent of a search can be determined to asufficient degree of certainty, a “one box” result may be provided, suchas in the form of a summary of actual information that is responsive toa query. For example, when “weather” is the query, a preformatteddisplay of current weather conditions and a multi-day forecast may beshown, along with hyperlinks to more detailed information such asanimated weather maps. Where a one box can be determined, it may bedisplayed initially, apart from any particular categories ofinformation, and a user may then select controls associated withparticular categories of results. Also, a list of results may bedisplayed below a one box.

In some implementations, as shown in the screen shots 302, 323 and 335,each category of information is displayed with various navigation toolsto allow a user to easily navigate to other kinds of information. Forexample, the screenshot 302 illustrates navigation controls 338 thatallow a user to quickly navigate from map information to, for example,web information, image information or news information. Othernavigational controls may be provided in conjunction with certaincategories of information. For example, as shown in the screenshot 335,a navigation bar 347 can be provided with news information, to allow auser to navigate from query-specific news information (e.g., newsinformation related to “Starbucks”) to more general news categories(e.g., world news, U.S. news, business news, etc.).

In FIGS. 3A and 3B, the navigation controls 338 are all shown ordered ina single order; as described more fully below, however, tile orderingmay change depending on the context, such that, for example, the mostrelevant group of results has its corresponding navigation controldisplayed more prominently (e.g., in the left-most positions) than thecontrols for other groups. Such ordering of controls may be particularlybeneficial on smaller displays, where controls for all relevantcategories cannot be easily displayed at one time.

Various indicators may be provided to alert a user to the category ofinformation he or she is currently accessing. For example, in thescreenshot 302, “maps” is shown in bold and is not underlined (i.e.,lacking a hyperlink that can be selected), indicating that mapinformation is currently being displayed, whereas “web,” “images” and“news” are underlined, indicating their status as links to othercategories of information. As another example, supplemental indicatorsmay be provided to indicate the category of information being displayed,such as the “maps” indicators 341A and 341B in the screenshot 302, orthe “news” indicator 344 in the screenshot 335. Also, where tabs areprovided, the selected tab can be shown to connect directly to theresults display, while other tabs may have horizontal lines separatingthem from the results, in a conventional manner for displaying tabbedinterface elements.

Results can also be displayed be category in vertically alignedexpandable modules, with a title for each module displayed, and with themost relevant module at the top. Selection of a title van cause thecorresponding module to expand into a box showing additionalinformation, and additional selection can cause the box to collapse backto the next title. In addition, categories may be displayed onside-by-side scrollable cards, with or without indicators on each sideof a display showing which category is to the left or right.

In the above examples, each category of information corresponds to asingle query, but in some implementations, a user can enter a new querythat causes information to be displayed in response to the new query.For example, a user could enter a new query in the query box (e.g.,query box 350) shown in each of the screenshots 302, 323 or 335, andinitiate the new search for information related to the new query byselecting the corresponding “search” control (e.g., search control 353).Once the new search has been run, various categories of information maybe available that are related to the new search query (e.g., mapsinformation, web information, news information, image information,etc.).

As shown in FIG. 3B, various categories of information may be availablein response to a query for “Steven Spielberg,” including, for example,news information, as shown in screenshot 356; image information, asshown in screenshot 359; and web information, as shown in screenshot362. In some implementations, certain categories of information may onlybe available in response to certain queries. For example, no mapinformation or stock information may be available in response to a queryfor “Steven Spielberg.”

In some implementations, the category of information that is displayedin response to a query is selected by a default configuration parameter.For example, for queries received by certain computing devices, such asnon-mobile desktop machines, web information may always be presented toa user, along with the option to select other categories ofquery-responsive information (e.g., image information, news information,map information, etc.).

In other implementations, different categories of information may havededicated initial search pages, and the category of information that isfirst presented may be based on which dedicated initial search page theuser employs to enter the search query. In particular, map informationmay be associated with a dedicated maps search page, and by default, mapinformation may be initially provided in response to queries receivedfrom the dedicated maps search page-even though controls may also beprovided, such as the controls 338, that enable a user to receive othercategories of information in response to the same query. Similarly,image information may be associated with a dedicated image search page,and by default, image information may initially be provided in responseto queries received from the dedicated image search page-even though auser may be able to receive other categories of information throughselection of various controls, such as the controls 338.

The categories may be ranked for display according to rules or scoresgenerated in various manners, such as according to the machine learningapproach discussed above. In still other implementations, as isdescribed in greater detail below, the category of information that isfirst presented to a user may depend on a process that factors inparameters such as statistics associated with a specific query, the typeof device from which the query is received (“mobile,” “non-mobile,”handheld, device with a 4.5 inch screen, device with a 1.2 inch screen,etc.), a profile corresponding to a user account that is associated witha query, some combination of the above parameters, or other parameters.Each of these parameters is discussed in turn here.

In a system that receives queries from users and provides variouscategories of information in response (e.g., the information provider103), certain queries may be repeatedly received and processed. Forexample, a system that processes tens of thousands of queries or moreeach day may receive tens or hundreds of queries each day for both“Starbucks” and “Steven Spielberg.” In some implementations, the systemcan be programmed to analyze the desired category of information mostoften associated with each of the queries. For example, the system maybe able to determine that users entering queries for “Starbucks” aregenerally interested in finding articles about Starbucks' corporatepolicies, or maps showing locations of nearby Starbucks coffee shops,and that users are generally less interested in finding stockinformation about Starbucks or image information related to Starbucksmerchandise. As another example, the system may be able to determinethat users entering queries for “Steven Spielberg” are generally mostinterested in locating news articles reporting on Hollywood gossiprelated to Steven Spielberg or images showing pictures of the director,and users are generally less interested in finding map information orstock information related to “Steven Spielberg,” as such information maynot even be “relevant” (i.e., generally associated with a search queryfor “Steven Spielberg”).

The system can be programmed to determine, in a number of ways, thecategory of information in which users who enter specific queries aremost interested. For example, the system could determine a dedicatedsearch page from which a specific query is most often received. Inparticular, if queries for “Steven Spielberg” are most often receivedfrom an “image” search page, the system may correlate queries for“Steven Spielberg” with image information; similarly, if queries for“Starbucks” are most often received from a “maps” search page, thesystem can correlate queries for “Starbucks” with location information.

As another example of a method by which a system can determine thecategory of information in which users are most interested, the systemcan track users' interactions with results that are provided in responseto queries. in particular, the system may analyze user navigationalinput received following a user's receipt of search results to determinea category of information to which the user navigates. For example, whenweb information is provided (e.g., by default) to users in response to aquery for “Steven Spielberg” (e.g., as shown in the screen shot 362),the system may determine that users most frequently select the control338B to navigate to image information (e.g., as shown in the screen shot359) before or instead of selecting any of the web results, or beforeselecting any other controls. Accordingly, the system may correlatequeries for “Steven Spielberg” with image information. Similarly, tilesystem may determine that when web information is provided (e.g., bydefault) to users in response to a query for “Starbucks,” users mostfrequently select a control (e.g., control 338D) to navigate to mapinformation (e.g., as shown in the screen shot 302). Accordingly, thesystem may correlate queries for “Starbucks” with map information.

Another user interaction that the system may analyze is time spentaccessing various categories of information. In particular, the systemmay determine that users access map information, image information andnews information in response to a query for “Starbucks,” but that usersspend the most time viewing and manipulating map information andrelatively little time viewing or accessing image information.Accordingly, the system may correlate map information with queries for“Starbucks.”

Based on various methods of determining a likely category of informationthat users are looking for in response to their specific queries, thesystem can, in some implementations, develop scores or rules forparticular queries, or statistics over time corresponding to eachdistinct and periodically received query. A machine learning approachfor forming correlations by analyzing logs of training data is discussedabove. In addition, other statistical approaches may be performed,either in aggregated data apart from the actions of particular users, oralso in combination with data about a particular user's actions in theform of a user profile. For example, over a 1-month period, the systemmay receive 12,606 queries for “Starbucks.” Of these 12,606 queries, thesystem may determine that in 1,624 instances, (about 13% of the time)the user was looking for news information and in 7,154 instances, (about57% of the time) the user was looking for map information. Based on thisdata (e.g., a “distribution”), the system could predict that users whosubmit queries for “Starbucks” are generally interested in newsinformation. Thus, in some implementations, the system could presentnews information in response to queries for “Starbucks” and more oftenthan not, this may be the information a specific user is seeking inresponse to a query for “Starbucks,” if the i 2,606 queries over the1-month period are representative of all queries for “Starbucks.”

Other information may be known about each (or some) of the 12,606example queries for “Starbucks.” For example, based on certainmeta-information that may be received with a query itself, the systemmay be able to determine whether a specific query is received from adevice that is relatively “mobile” or from a device that is deemed“non-mobile” (or classified as such for purposes of this exampleanalysis). In particular, search queries that are received from wirelesscommunication devices (e.g., cell phones or smartphones) may generallyinclude meta information identifying certain wireless network providers(e.g., Verizon, Cingular, T-Mobile, etc.), whereas search queries thatare received from, tor example, non-mobile desktop computers may notinclude such meta information.

Because information about devices from which queries are received may beuseful in predicting physical characteristics of the electronic devicefrom which a specific query is received-which in turn may be useful forpredicting the category of information a specific user is seeking—it maybe advantageous for the system to maintain separate statistics (e.g.,“sub-distributions”) for specific queries based on whether the queriesare received from an electronic device that is classified as a “mobile”device or from an electronic device that is classified as a “non-mobile”device. For example, users of mobile devices (e.g., smartphones or cellphones) who enter queries for “Starbucks” may generally be interested infinding map information, whereas users of non-mobile devices (e.g.,desktop computers) who enter tile same queries may generally beinterested in finding web information. As another example, users ofmobile devices who enter queries for “Steven Spielberg” may generally beinterested in finding news information, whereas users of non-mobiledevices who enter the same query may generally be interested in findingimage information. Thus, by analyzing various query-related informationassociated with specific queries, the system can maintain certainstatistics for the specific queries and can classify and categorize thestatistics in many different ways, such as by the type of electronicdevice (e.g., “mobile” or “non-mobile”) from which the queries arereceived. Two exemplary tables of query-specific statistics arepresented below.

Table 1 illustrates example statistics for the queries “Starbucks” and“Steven Spielberg,” when those queries are received from a device thattile system classifies as “non-mobile.” Table 2 illustrates examplestatistics for the same two queries, when those queries are receivedfrom a device that the system classifies as “mobile.” The particularstatistics may be indicative of non-statistical relationships analyzedby the system (such as in the machine learning example above), or ofdirect statistical analyses performed by the system.

TABLE 1 Exemplary distributions for queries received from “non-mobile”devices. Web Image News Maps Stocks Infor- Infor- Infor- Infor- Infor-mation mation mation mation mation “Starbucks” 32% 13% 24% 19% 12%“Steven Spielberg” 47% 24% 29%  0%  0%

TABLE 2 Exemplary distributions for queries received from “mobile”devices. Web Image News Maps Stocks Infor- Infor- Infor- Infor- Infor-mation mation mation mation mation “Starbucks”  5%,  1% 15%, 73% 6%,“Steven Spielberg” 21%, 17% 62%   0% 0% 

In each table, the percentages represent determined or calculateddistributions for a specific query relative to a specific category ofinformation. For example, as depicted in Table 1, a query for“Starbucks” that is received from a “non-mobile” device, is determinedto be associated with web information 32% of the time (i.e., 32% of theanalyzed queries for “Starbucks” that were received from an electronicdevices deemed to be “non-mobile” (e.g., devices for which nocorresponding wireless network provider meta information was received)during a specific example period were determined to be targeted to webinformation (based, for example, on a search page from which the querywas received, navigational actions of users following receipt ofinformation provided in a response to the query or time spent by usersin accessing various categories of information related to the query)).As another example, as depicted in Table 2, a query for “Starbucks” thatis received from a “mobile” device is determined to be associated withmap information 77% of the time. Tables 1 and 2 provide other examplestatistics for queries for “Steven Spielberg” received from “mobile” and“non-mobile” devices.

In some implementations, a system can use such distributions to enhanceusers' search experiences. For example, based on the exampledistributions shown in Tables 1 and 2, the system could associate asearch query for “Starbucks” from a “mobile” device with mapinformation, and a search query for “Steven Spielberg” from a“non-mobile” device with news information-possibly saving a large numberof specific users the trouble of navigating from a default category ofinformation (e.g., web information) to an intended category ofinformation.

In the examples provided in Tables 1 and 2, the distributions of queriesare fully characterized. That is, the percentages total 100% for eachquery. However, the reader will appreciate that the information depictedin Tables 1 and 2 would still be useful, even if only a fraction of thequeries were characterized. For example, specific queries could becorrelated to the category of information that was most often associatedwith the queries that were characterized, regardless of the actualpercentages.

In some implementations, a system can predict the category ofinformation a specific user is seeking based on a profile that thesystem maintains for that user. For example, using various techniques,some of which are described above, a system can determine a distributionof the categories of information a specific user generally accesses. Insome implementations, such distributions can be determined independentlyof the content of tile corresponding search queries. For example, withreference to Joe and Jane, the system may be able to determine that Joegenerally accesses map information, whereas Jane generally accessesstock information. An example distribution that may be included in aprofile that the system maintains (e.g., develops over time) for Joe andJane is provided in Table 3, below.

TABLE 3 Exemplary profiles for two users Web Image News Maps StockInfor- Infor- Infor- Infor- Infor- mation mation mation mation mationJoe (student: cell 8% 21% 17% 53%  1% phone user) Jane (stockbroker; 1% 3% 26% 19% 51% smartphone user)

In some implementations, a system can use profile-based distributioninformation such as that depicted in Table 3 to enhance users' searchexperiences. For example, based on the example distributions shown inTable 3, the system could associate all queries from Joe with mapinformation and all queries from Jane with stock information-possiblysaving both Joe and Jane the trouble, at least some of the time, ofhaving to navigate from a default category of information (e.g., webinformation) to an intended category of information.

In some implementations, further advantages can be provided by a systemthat determines a category of information a specific user is likelyseeking based both on global distribution information associated withthe specific corresponding query and profile information associated withthe user from whom the query is received. In some implementations,multiple categories of information are provided to the user in responseto a search query, but the categories of information are ordered basedon a determined likelihood that the user is looking for a specificcategory of information. One example method of using both global,query-based distribution information and user-profile information toorder the categories of information to be provided to a user in responseto a query is provided with reference to Equation 1, Tables 4-8 andFIGS. 4A and 4B.

One method of combining global, query-specific, distribution informationand user-profile information is to calculate a likelihood that aspecific user is searching for each possible category of informationassociated with the specific query, based on a weighted contribution ofa global distribution for the specific query for “non-mobile” devices, aglobal distribution for the specific query for “mobile” devices, and aquery-independent, profile-based distribution. For example, each time auser enters a search query, the system can retrieve the user's profile,if such a profile exists (e.g., a distribution of the categories ofinformation the user generally reviews as depicted in Table 3) and thesystem can retrieve global query-specific profiles (if they areavailable) for “mobile” and “non-mobile” devices (e.g., query-specificprofiles as depicted in Tables 1 and 2). Based on the retrievedprofiles, the system can in some implementations, calculate a likelihoodthat the user is seeking a particular category of information based oncalculations associated with each category of available information.

In one implementation, a calculated likelihood can be expressed as afunction of the user, the query, and a series of weighting factors thatcan be used to adjust the relative impact of the user's profile and thequery-specific “non-mobile” and “mobile” distributions. For example, thelikelihood that a specific user (USER) is searching for a particularcategory of information (INFOTYPE) in response to a particular query(QUERY) can be calculated by the following:LIKELIHOOD(INFOTYPE,USER,QUERY}=WEIGHT_(PROFILE)*PROFILE(USER,INFOTYPE)+WEIGHT_(NMDEVICE)*NMDIST(QUERY,INFOTYPE)+WEIGHT_(MDEVICE)*MOIST(QUERY,INFOTYPE)  Equation1:The various expressions in Equation 1, according to one implementation,are described below:

LIKELIHOOD(INFOTYPE, An overall calculation representing, for example, astatistical USER, QUERY) likelihood that a query (QUEF-!Y) submitted bya user (USEF-!) is directed to a particular category of information(INFOTYPE). Note that the “user can be a human user identifiable, forexample, by a user account ID; or the “user” could be a specificelectronic device, identifiable, for example, by a device ID.PROFILE(USER, A number included in a profile for a user (USER) thatrepresents a INFOTYPE) frequency with which the user accesses aparticular category of information. For example, in someimplementations, PROFILE(JOE, WEBINFO) = 0.08 means that a profile foruser Joe indicates that 8% of Joe's data accesses correspond to thecategory of information classified as “web information” (WEBINFO).WEIGHT_(PROFILE) A number that represents the weighting factor forprofile information. In this example, the weighting factor determineshow much of an impact on the overall likelihood calculation profileinformation will have. NMDIST(QUERY, A number, included for example in aglobal query-based distribution, INFOTYPE) representing a frequency withwhich a query (QUERY) is globally correlated to a particular category ofinformation (INFOTYPE) for devices that are classified as “non-mobile.”Note that devices may be classified as “non-mobile” for purposes of thisdistribution, even though they may technically be portable devices. Asan example, in some implementations, NMDIST(“STARBUCKS”, STOCKINFO) =0.12 means that a query for “Starbucks” received from a device that isclassified as “non-mobile” has a 12% chance of being directed to stockinformation (e.g., based on an analysis of a large number usersprocessing information provided in response to queries for “Starbucks”).The “non-mobile device distribution” number may or may not be normalized(e.g., relative to other possible categories of information). In otherwords, in some implementations, the number is a percentage likelihood;in other implementations, the number provides only a relative(non-normalized) basis for comparison to other numbers.WEIGHT_(NM)_DEVICE A number that represents the weighting factor for“non--mobile” query- specific information. In some implementations, theweighting factor determines how much of an impact on the overalllikelihood calculation “non-mobile” query-specific information willhave. MDIST(QUERY, A number, included for example in a globalquery-based distribution, INFOTYPE) representing a frequency with whicha query (QUERY) is globally correlated to a particular category ofinformation (INFOTYPE) for devices that are classified as “mobile.” Notethat devices may be classified as “mobile” for purposes of thisdistribution, even though they may technically be devices that aredifficult to transport. As an example, in some implementations,MDIST(“STARBUCKS”, IMAGEINFO) = 0.01 means that a query for “Starbucks”received from a device that is classified as “mobile” has a 1% chance ofbeing directed to image information (e.g., based on an analysis of alarge number users processing information provided in response toqueries for “Starbucks”). The “mobile device distribution” number may ormay not be normalized (e.g., relative to other possible categories ofinformation). WEIGHTMDEVICE A number that represents the weightingfactor for “mobile” query- specific information. In someimplementations, the weighting factor determines how much of an impacton the overall likelihood calculation “mobile” query-specificinformation will have.

In some implementations, the weighting factors can be global constantsthat are applied to each user and query. One set of possible weightingfactors are provided in Table 4. As shown in Table 4, the user-profileweighting factor is 70%, indicating that the user profile contributesmost significantly to tile overall likelihood calculation, whereas theglobal “non-mobile” and “mobile” distributions each contribute lesssignificantly to the overall likelihood calculation.

TABLE 4 Example weighting factors for determining a likely ranking ofdesired categories of information User “Non-mobile” Device “Mobile”device profile Distribution Distribution 70% 10% 20%

Other weighing factors and equations for calculating a likelihood valueare possible. For example, in some implementations, only “non-mobile”query-specific distributions may be included in the likelihoodcalculation when the corresponding query is received from a device thatis classified as ‘non-mobile.” As another example, profile informationmay not be available for each query received, and in such cases whereprofile information is not available, the corresponding likelihoodcalculation may be based solely on one or more of a “non-mobile” and“mobile” query-specific distribution information.

Distributions could also be maintained and classified in different ways.For example, separate distributions may be maintained for individualelectronic devices (e.g., one distribution for Motorola RAZR™ cellphones, another for Palm Treo™ smartphones, another distribution for LGcell phones, etc.), and an appropriate distribution may be applied tospecific likelihood calculations based on the type of device from whichcorresponding queries are received (e.g., in cases in which device typecan be determined). As another example, distributions could bemaintained and classified based on a time of day at which they arereceived, or on a geographic location from which they are received. Thereader will appreciate that numerous other equations, methods, anddistributions can be applied to the likelihood calculation withoutdeparting from the spirit and scope of this description.

Example likelihood calculations for correlating a specific query from aspecific user to a particular category of information are furtherexplained with reference to example numbers in Tables 5-8. Table 5illustrates numerically how a likelihood calculation may be made—in aparticular, a likelihood calculation relating to whether a particularuser (Joe) is seeking each possible category of information (webinformation, image information, news information, map information, orstock information—in one example) in response to a search query(“Starbucks”). The numbers in Table 5 are calculated based on Equation 1and the contents of Tables 1, 2, 3 and 4.

TABLE 5 Example Category-Query Calculations (e.g., for a “Starbucks”Query from Joe) WEB INFORMATION 0.098 LIKELIHOOD (WEBINFO, JOE,“STARBUCKS”) = WEIGHT_(PROFILE) ¹ * PROFILE (JOE, WEBINFO)² +WEGHT_(NMDEVICE) * NMDIST(“STARBUCKS”, WEBINFO)³ + WEIGHT_(MDEVICE) *MDIST(“STARBUCKS”, WEBINFO)⁴ = (0.7) * (0.08) + (0.1) * (0.32) + (0.2) *(0.05) = 0.098 IMAGE INFORMATION 0.162 LIKELIHOOD (IMAGEINFO, JOE,“STARBUCKS”) = WEIGHT_(PROFILE) * PROFILE (JOE, IMAGEINFO) +WEIGHT_(NMDEVICE) * NMDIST(“STARBUCKS”, IMAGEINFO) WEIGHT_(MDEVICE) *MDIST(“STARBUCKS”, IMAGEINFO) = (0.7) * (0.21) + (0.1) * (0.13) +(0.2) * (0.01) = 0.162 NEWS INFORMATION 0.173 LIKELIHOOD (NEWSINFO, JOE,“STARBUCKS”) = (0.7) * (0.17) + (0.1) * (0.24) + (0.2) * (0.15) = 0.173MAP INFORMATION 0.536 LIKELIHOOD (MAPINFO, JOE, “STARBUCKS”) = (0.7) *(0.53) + (0.1) * (0.19) + (0.2) * (0.73) = 0.536 STOCK INFORMATION 0.031LIKELIHOOD (STOCKINFO, JOE, “STARBUCKS”) = (0.7) * (0.01) + (0.1) *(0.12) + (0.2) * (0.06) = 0.031 ¹Example weighting constants taken fromTable 4. ²Example profile number taken from Table 3. ³Example“non-mobile” device distributions taken from Table 1. ⁴Example “mobile”device distributions taken from Table 2.

As shown in one implementation in Table 5, a likelihood value of 0.098corresponds to web information, a likelihood value of 0.162 correspondsto image information, likelihood value of 0.173 corresponds to newsinformation, a likelihood value of 0.536 corresponds to map information,and a likelihood value of 0.031 corresponds to Stock Information. Usingthese calculated likelihood values, a system may determine that thecategories of information that are most likely to be relevant to Joe, inresponse to a query for “Starbucks” are, in order of calculatedrelevance, map information, news information, image information, webinformation and stock information.

The system can use these calculated relevance values in providingcategories of responsive information to the query for “Starbucks”associated with Joe. In particular, in some implementations, the systemcould present map information first, followed by news information,followed by image information, and so on. In other implementations, thesystem could provide a single category of information (e.g., thecategory of information calculated to be most relevant to a specificquery) but provide a method by which the other information could beeasily obtained. For example, in response to a query for “Starbucks”associated with Joe and received from a device classified as “mobile”(e.g., Joe's cell phone 115), the system (e.g., the information provider103) could transmit a formatted response that includes only mapinformation, as shown in FIG. 4A. In the implementation shown, theformatted response includes links to other categories of information,and these other categories of information can be ordered based on thecorresponding calculated relevance between each category of informationand the original query.

In particular, FIG. 4A illustrates a navigation bar 402 that may allowthe user of cell phone 115 to navigate (e.g., scroll) from one categoryof information to another. For example, upon selection of a rightnavigation key (not shown) on the cell phone 115, the elements of thenavigation bar 402 may scroll left, such that “news” is displayed as theleft-most element, followed by “images,” followed by “web” (notcurrently shown); and news information may be displayed in the displayregion 405 of the cell phone 115. As another example, a user may be ableto navigate to another category of information by selecting a navigationcontrol 408A, 408B or 408C included in the navigation bar 402. Forexample, by selecting the “images” control 408B (e.g., by manipulationof keys (not shown) in the cell phone 115, or by touching the displayedcontrol element in the case of a touch-sensitive screen on the cellphone 115), information may be displayed in the display region 405, andthe navigation bar 402 may be redrawn to show the “images” control 408Bin the left-most position of the navigation bar 402.

In various implementations, the predictive presentation of informationcan significantly improve a user's experience. For example, byreceiving, in response to his query for “Starbucks,” the map informationthat is shown in FIG. 4A, Joe may avoid having to pre-select a desiredcategory of information or to navigate from an undesired defaultcategory of information to the desired category of information. Even incases in which system incorrectly predicts a user's desired category ofinformation, the user may still be able to, on average, obtain thedesired category of information faster than if the system had required apre-selection of the desired category of information or had provided adefault category of information in response to the user's query.

Additional aspects to an example system are illustrated and describedwith reference to Table 6 and FIG. 4B.

TABLE 6 Example Category-Query-Calculations (e.g., for a “StevenSpielberg” Query from Joe) WEB INFORMATION 0.145 LIKELIHOOD (WEBINFO,JOE, “STEVEN SPIELBERG”) = (0.7) * (0.08) + (0.1) * (0.47) + (0.2) *(0.21) = 0.145 IMAGE INFORMATION 0.205 LIKELIHOOD (IMAGEINFO, JOE.,“STE.VEN SPIELBERG”) = (0.7) * (0.21) + (0.1) * (0.24) + (0.2) * (0.17)= 0.205 NEWS INFORMATION 0.272 LIKELIHOOD (NEWSINFO, JOE, “STEVENSPIELBERG”) = (0.7) * (0.17) + (0.1) * (0.29) + (0.2) * (0.62) 0.272 MAPINFORMATION 0.371 LIKELIHOOD (MAPINFO, JOE, “STEVEN SPIELBERG”) =(0.000) (0.7) * (0.53) + (0.1) * (0.00) + (0.2) * (0.00) = 0.371 STOCKINFORMATION 0.007 LIKELIHOOD (STOCKINFO, JOE, “STEVEN (0.000)SPIELBERG”) = (0.7) * (0.01) + (0.1) * (0.00) + (0.2) * (0.00) = 0.007

As shown in Table 6, different likelihood values are shown for a queryfor “Steven Spielberg” associated with Joe. Table 6 illustrates specialfiltering that can be applied, in some implementations, to certaincategories of information. For example, as shown in Tables 1, 2 and 6,the query for “Steven Spielberg” is not correlated with either mapinformation or stock information. However, applying example Equation 1to the profile and distribution numbers in Tables 1 and 2 results innon-zero likelihood values for both map and stock information, eventhough neither map nor stock information may be correlated with “StevenSpielberg” (at least for purposes of this example).

Accordingly, certain likelihood values for certain categories ofinformation may be overridden if they are not likely to predict ameaningful correlation between a specific user and query and aparticular category of information. For example, in someimplementations, a calculated likelihood value may be reset to zero ifany of the underlying values from which it was calculated are below acertain threshold. The resetting of likelihood values is depicted by theparenthetical zero values for map information and stock information inTable 6. In a case where calculated values are overridden, as shown, thesystem may order the categories of information in response to Joe'squery for “Steven Spielberg” as follows: news information, imageinformation and web information. Had the values not been overridden, thesystem may have ordered map information first, even though no mapinformation may be correlated with “Steven Spielberg.” FIG. 48illustrates one example of how various categories of information can bepresented (e.g., in an order based on calculated relevance values) inresponse to a query for “Steven Spielberg” associated with Joe (Joe'scell phone 115).

Tables 7 and 8 and corresponding FIGS. 4C and 4D illustrate examples ofhow categories of information may be ranked based on a calculatedrelevance and presented for display in a smart phone 118 in response tosimilarly queries (“Starbucks” and “Steven Spielberg”) received from adifferent user.

TABLE 7 Example Category-Query-Calculations (e.g., for a “Starbucks”Query from Jane) WEB INFORMATION 0.049 LIKELIHOOD (WEBINFO, JANE,“STARBUCKS”) = (0.7) * (0.01) + (0.1) * (0.32) + (0.2) * (0.05) = 0.049IMAGE INFORMATION 0.036 LIKELIHOOD (IMAGEINFO, JANE, “STARBUCKS”) =(0.7) * (0.03) + (0.1) * (0.13) + (0.2) * (0.01) = 0.036 NEWSINFORMATION 0.236 LIKELIHOOD (NEWSINFO, JANE, “STARBUCKS”) = (0.7) *(0.26) + (0.1) * (0.24) + (0.2) * (0.15) = 0.236 MAP INFORMATION 0.298LIKELIHOOD (MAPINFO, JANE, “STARBUCKS”) = (6.7) * (0.19) + (0.1) *(0.19) + (0.2) * (0.73) = 0.298 STOCK INFORMATION 0.381 LIKELIHOOD(STOCKINFO, JANE, “STARBUCKS”) = (0.7) * (0.51) + (0.1) * (0.12) +(0.2) * (0.06) = 0.381

TABLE 8 Example Category-Query-Calculations (e.g., for a “StevenSpielberg” Query from Jane) WEB INFORMATION 0.096 LIKELIHOOD (WEBINFO,JANE, “STEVEN SPIELBERG”) = (0.7) * (0.01) + (0.1) * (0.47) + (0.2) *(0.21) = 0.096 IMAGE INFORMATION 0.079 LIKELIHOOD (IMAGEINFO, JANE,“STEVEN SPIELBERG”) = (0.7) * (0.03) + (0.1) * (0.24) + (0.2) * (0.17) =0.079 NEWS INFORMATION 0.335 LIKELIHOOD (NEWSINFO, JANE, “STEVENSPIELBERG”) = (0.7) * (0.26) + (0.1) * (0.29) + (0.2) * (0.62) = 0.335MAP INFORMATION 0.133 LIKELIHOOD (MAPINFO, JANE, “STEVEN SPIELBERG”) =(0.000) (0.7) * (0.19) + (0.1) * (0.00) + (0.2) * (0.00) = 0.133 STOCKINFORMATION 0.357 LIKELIHOOD (STOCKINFO, JANE, “STEVEN (0.600)SPIELBERG”) = (0.7) * (0.51) + (0.1) * (0.00) + (0.2) * (0.00) = 0.357

Taken together, the Tables 5-8 and corresponding FIGS. 4A-40 illustrateexamples of how categories of information can be predicatively orderedbased on one or more calculations related to a user profile associatedwith a specific query (e.g., compare FIGS. 4A and 4C or 48 and 40 ) andcalculations related to statistics about the query itself (e.g., compareFIGS. 4A and 4B or 4C and 40 ). As mentioned above, such predictiveordering can, in some implementations, enhance a user's experience withreceiving information that is responsive to a query.

FIG. 5 is a flow diagram illustrating an example process 500 by which auser of an electronic device (e.g., a cell phone or smart phone) cangenerate a search query, transmit the search query to an informationprovide, and in response, receive search results that include differentcategories of information that are ordered based on a predicted categoryof information for which the user may be searching. For clarity, theexample actions in the process 500 are depicted as occurring at a mobiledevice, one or more search engines, a response formatter, a profilemanager and a relevance filter, but the reader will appreciate that theactions or similar actions could also be carried out by fewer devices orsites or with a different arrangement of devices or sites.

As shown, a user of an electronic device (e.g., a mobile device) cangenerate (501) a search query and transmit (504) the search query to aninformation provider. For example, referring to FIG. 1 , a user of thecell phone 115 can generate (501) a search query and direct that queryto the information provider 103. Physically, the cell phone 1 i 5 cantransmit the query via the wireless network 124 and the network 121(e.g., tile internet) to tile information provider 103 (via parts A andB).

The information provider can receive (507) tile query (e.g., inparticular, for example, a search engine within the information providercan receive the query). Once received, the search query can be executed(520) by the search engine. For example, referring to FIG. 2 , theinformation provider 102 can receive (507) the search query via theinterface 222, and the request processor 225 can reformat the searchquery, if necessary, and transmit the (reformatted) search query to thesearch engine 201, which can execute (510) the search query. Executing(510) the search query can including searching various indexes, such asthe web index 204, the maps index 207, the news index 210, etc., forcontent that corresponds to contents of the search query. In someimplementations, the search engine 20 i executes the search queryagainst each indexed category of information in order to identify allpossible categories of information that may be relevant to the searchquery.

A response formatter can receive (513) the search results. For example,after the search engine 201 executes (510) the search query against eachpossible index to identify multiple sets of results, each resultincluding a different category of information (e.g., web information,maps information, news information, etc.), the search engine 201 canforward the results to the response formatter 228. The responseformatter can determine an appropriate order for the differentcategories of search results (i.e., the response formatter can determinewhether to present the search results to the mobile device in, forexample, a web-image-news order, a map-news-image order, anews-web-images, etc.).

To determine the order in which different categories of information arepresented to the electronic device, the response formatter can, in someimplementations, employ a combination of user profile data and globaldistribution data associated with a specific query. To do so, theresponse formatter can request (517) a profile corresponding to theelectronic device from which the query was received, or a userassociated with that device.

A profile manager can retrieve (520) and provide (520) the profile inresponse to the response formatter requesting (517) the profile, and theresponse formatter can receive (523) the profile. For example, withreference to FIG. 2 , the response formatter 228 can send a request forspecific profile information to the profile manager 231, which canretrieve (520) the appropriate profile from, for example, the profiledatabase 234. In some implementations, information about the electronicdevice from which the query is received or about a user of theelectronic device is included in the query itself. For example, thequery may include a device identifier corresponding to the electronicdevice or a user identifier (e.g., a user login or account identifier)corresponding to a user of the electronic device; in such cases, therequest processor 225 can extract this information from the query andprovide it to the response formatter 228 for use in identifying andobtaining an appropriate profile.

The response formatter can also request (526) relevance information froma relevance filter. The relevance filter can determine (529) and provide(529) the relevance information corresponding to a specific query to theresponse formatter, which, in turn, can receive (532) the relevanceinformation. For example, with reference to FIG. 2 , the responseformatter 228 can request relevance information from the relevancefilter 237 corresponding to the specific query. In some implementations,as is described above, relevance information can include a likelihoodthat the search query is associated with each category of information(e.g., as depicted by Tables 1 and 2).

Based on the profile information and/or relevance information, theresponse formatter can order (535) the search results, based on, forexample, a calculated likelihood that the query is directed to aparticular category of information. For example, the response formattercan, in some implementations, calculate a likelihood (e.g., using amethod similar to that described above with reference to Equation 1 andTable 3-8) that the received query is directed to each category ofinformation, and each result set can be ordered (535) based on thecategory of information to which the result set corresponds and thelikelihood that the category of information is the category to which thequery was directed.

In addition, the ordering of the results may also be based on thequality of the actual results. Specifically, even if a query isdetermined to be a good query for a particular group or corpus ofinformation, the result may not be shown or may be demoted if the resultis bad. The quality of the result may be determined, for example, by thenumber of relevant documents found it a corpus, by scores of theidentified documents as determined by a search engine scoring system, bythe similarity of the identified documents to each other, or by otherappropriate mechanisms.

Each result set can also be formatted (538) as necessary, transmitted(541) to the electronic device from which the query was received, andthat electronic device can receive (544) the results set and display theresult sets to a user of the electronic device. For example, uponcompletion of the ordering (535) and formatting (538) processes, theresponse formatter 228 can transmit (541) the ordered, formatted resultsets to the mobile device 115 via the interface 222, network 121 (seeFIG. 1 ) and wireless network 124 (e.g., via paths E and F). In someimplementations, the results are displayed in the mobile device 115 asshown in FIGS. 4A and 4B.

FIG. 6 is a screen shot 600 showing an exemplary local one box. Thescreen shot 600 shows a display on a large device like a desktopcomputer, but the one box could also be displayed in a more compactmanner on the mobile device display. Where mobile devices are involved,so-called “local” search results may be particularly relevant to users.Local results are results drawn to a particular geographic area, such asrestaurants or other stores in an area, or to parameters such asweather, sports scores, and certain (local) news. Because people tend tosearch for restaurants, look at the news, or comparison shop on priceswhile using mobile devices (and tend to conduct research and other moreinvolved activities at home computers), such local search results oftencome up when searches are presented on mobile devices.

Screen shot 600 shows one exemplary manner in which certain localresults may be displayed. The results here are results generated for asearch on “Starbucks.” The search engine has recognized that searchquery as involving a local search and has generated a local one box,along with other related information responsive to the search. Locationarea 602 indicates to the user that the search has been interpreted asbeing local-related, and provides a user with the opportunity to enter adifferent area, such as by state, municipality, or zip or area code. IIthe user enters such an area, then the default location for the user'sdevice may be changed to that area.

One box area 604 shows multiple formatted local results for the searchquery-in this case, contact information for particular Starbucks stores.The information has been extracted from other sources, such as webpages, and particularly relevant information is shown in one box area604. Other formats for a one box, which takes from one or more articlesparticular information that is likely to be highly relevant, and formatsportions of that material from the articles into a more readable form,may also be used. As explained elsewhere, for example, a weather one boxmay format current temperature and future high-low temperatureinformation into a single graphic.

Web results area 606 may display search results in a more traditionalmanner, with a link to a particular article, a snippet from the article,and a URL for the article. The web results may be selected in variousmanners. Additional groups area 608 allows a user to identify othercorpuses or groups that can be displayed. For example, an “images” link,if selected, can cause the display of a number of thumbnails relating tothe term “Starbucks,” such as photos of a coffee cup, a Starbucksoutlet, or pictures of Starbucks management. Such other groups can bedisplayed in case the decision to first display a local one box to auser was wrong, or because the user browses the local search results,and then decides to look in other groups.

FIG. 7A is a flow chart of a process 700 for identifying theapplicability of local search results to a query. In general, theprocess involves determining whether a particular query entered by auser is likely or unlikely to be directed to “local” search results. Forexample, terms or queries such as “restaurant,” “weather,” “movie,”“directions,” and “McDonalds” may be highly correlated with local searchrequests. In contrast, terms or queries like “Google,” “DavidHasselhoff,” “podcast,” or “Monty Python” would not.

In this manner, local search results may be considered unique among thevarious formats of results that might be displayed to a user in searchresult groups. In addition, proper placement of local results may beparticularly important for queries explicitly identified as local orqueries that come from mobile devices (as determined, e.g., by an IPaddress in the header of the message sending the query), because localsearch is so highly correlated with the needs of such users.

The process 700 shows an exemplary approach for determining whether aparticular search query is likely to be directed to a local search ornot, and to display the search results accordingly. At box 702, a queryis received, and may take any appropriate form for a query. At box 704,the process determines whether the query contains a location indicator.Location indicators may include, for example, 5-digit or 9-digit numbersthat match a zip code, a 3-digit number that matches an area code (anddoes not match up with surrounding terms in the query), the name of atown or state, or the abbreviation of a state (CA, MN, Cal, Calif, Minn,etc.), the letters for an airport (e.g., MSP, SFO, etc.), and well-knownvenue names (e.g., The Forum, Wrigley, Shea Stadium, etc.).

Certain explicit location identifiers may be less-strongly correlatedwith a user's desire to see local search results than are others. Forexample, a zip code may be highly correlated, as may a combination of acity name and state abbreviation (and especially when also accompaniedby a zip code). In contrast, a search for a venue name might only showan interest in non-local information relating to the venue (e.g., thename of the architect or general contractor for a stadium) or forinformation relating to a term in the name of the venue (e.g., a desirefor information about the politician Hubert Humphrey rather than localresults surrounding the Hubert H. Humphrey Metrodome). In suchsituations where the explicit location identifier may be consideredweak, the default location that has previously been associated with adevice of a user account may be used instead. Also, if the quality ofresults generated using the explicit identifier is low (e.g., there arenot many results, or the results are highly dissimilar to each other),the default location may be used.

If such an explicit identifier is found in the query, the process mayassume that the user wants to conduct a search for that area. Theprocess may then generate a location indicia for tile query (708). Thelocation indicia is a indicator of how closely correlated the query isto local search results. The indicia may, for example, have multiplediscrete values, may have a more continuous range of values, or may bedetermined according to the firing or non-firing of a number of rules.

One exemplary implementation using three discrete values may involve theuse of a white list and a black list, whose preparation is described inmore detail with respect to FIG. 7B. The white list contains queries orportions of queries that are highly correlated with local search, whilethe black list contains queries of portions of queries that have a lowcorrelation with local search.

As shown by process 750 in FIG. 7B, the lists may be prepared by firstlocating historical search data. The data may be gathered from twosources: (1) logs of searches for ordinary web data (box 752); and (2)logs of queries for “local” data (box 752), as determined, for example,by the fact that the queries were received with IP addresses associatedwith mobile data networks and/or were received when a “local” controlwas selected on a user's search application. Both categories of data mayalso have been associated with mobile devices, with the distinction madebased on the corpus (web or local) to which the user directed the query.

At box 756, the process forms “most common” lists, for example, the10,000 most common search queries for each collected group of data.These lists may be considered to represent the sorts of topics thatpeople interested in non-local web search are most interested in, andthose that people interested in local web search are most interested in.(The techniques here can be expanded to categories other than local andweb also.) At step 758, a blacklist of queries or terms that should notbe considered to be local-related is formed by taking the web “mostcommon” list for web searches, and removing those entries that alsoappear in the local “most common” list. By this process, the black listwill not contain any query that is frequently used by local searchers.

The reciprocal process may be conducted with respect to forming a whitelist. In particular, the local “most common” list may have its entriesdeleted that appear on the web most common list. In this manner, anyterms that users relatively frequently associated with something otherthan local search will be eliminated from being identified as highlylocal.

Returning to FIG. 7A, the white list and black list may be used togenerate a location indicia for the query (box 708). For example, if thequery is on the blacklist, the indicia may be a low or negative value,while if it is on the white list, the indicia may be a high or positivevalue, and if it is on neither list, the indicia may be zero or null.

Other techniques may also be used to generate the local indicia. Forexample, scores or rules for identifying a particular query (whether afull query or one or more portions of a query) may be generated throughthe use of a machine learning system in a manner like that describedabove. In particular, such a system may be trained with data identifiedas local and non-local and may generate a set of rules for identifyingfuture queries as being local or non-local. Such approaches may beimplemented using, for example, relevance filter 237 shown in FIG. 2 .

The process may also obtain search result sets for various groups (e.g.,web, local, maps, video, images, etc.) (box 710), and may then use thegenerated local indicia to determine a placement of the local resultsrelative to other groups of results. Also, the indicia may be used todetermine whether to display the local results as a one box (e.g., ifthe query is very highly correlated with local results), or as a list ofdiscrete results. In one example, the indicia may be used to place thelocal results in front of other categories of results if the indicia issufficiently high (e.g., the query is on a whitelist, the query has ahigh local score, or rules generated by a system indicate a high localcorrelation). Finally, the results may be presented in the determinedorder (box 714), and the process may end (box 715) and wait for furtherinput from the user. Where an explicit location indicator (e.g., zipcode) is included in a query, the corresponding local results may beautomatically promoted to the “top” group of results regardless of thepresence or absence of the rest of the query on a whitelist orblacklist, or regardless of whether the rest of the query is deemed tobe local or not. Where no explicit location indicator is included, thevarious mechanisms for assigning an indicia may be employed, and suchapproaches may be used to determine the appropriate location of a localresult group or groups among other groups of results.

If there is no identifiable location in the query (box 704), the processmay determine whether a default location has been associated with theremote device or user (box 706). For example, when a person first uses amobile device or application, they may have no location associated withthem. But when they conduct a search that includes a location or theyotherwise enter a location into the application, that location may bestored (either at the remote device or at a central server) and used asa “default” location for subsequent local searches. Thus, for example,when initially using a new telephone, a user may enter their home zipcode. If they then enter a query like “movie schedule,” the results forthe local search may be centered around their local zip code.

If a default location can be determined (box 706), then the process mayproceed to identify and order search results as described above (boxes708-715). If there is not a default location and there is no location inthe query itself, the user may be prompted for a location, and theoperations of boxes 708-715 may be run.

However, such prompting may be distracting to the user, particularly ifthe user is not interested in a local search. As a result, the process700 may generate search results and display them to the user (includingin an order for groups determined by the groups' relevance to the query,as described above). Depending on how correlated the query is to localsearches or results, the system may also then prompt the user to enterlocation information if they would like. To do so, the exemplary process700 shows the generation of location indicia for the query (box 716),which may occur in the various manners expressed above.

The value of the location indicia may control the manner in which afollow up request for local information is put to the user. For example,if the query is determined to be highly correlated to location, therequest may be placed above any search results in a prominent positionso that the user (who presumably wants good local results) can quicklysee that they have the option of entering location information, and canquickly get local results. II the correlation is low, on the other hand,the follow up request might not be shown at all, or it may be placed ina position of less prominence, so as to avoid distracting a user whopresumably is not interested in local results.

The process 700 then generates a location indicia for the query (box716) and also obtains search result sets for the query (box 718). Anylocal results may include an inferred location for the query, such as byestimating the remote device's location using techniques to determine anapproximate location of a wireless network end node, among othertechniques. The order of display of the local results relative to otherresults may then be determined using the indicia, as may the location ofa follow up request control that allows a user to enter locationinformation. For example, where the query is highly correlated withlocal results, the control may include a text entry box on the front tabof the displayed results and/or at the top of the displayed results.With the order of the results determined, the results may be displayedaccording to that order (box 722). Such display may occur by atransmission of the results from a central server to the remote devicein a communication formatted to display the results in the particularorder (and additionally, for example, in a ordered tab format discussedabove), followed by the display on the remote device.

When a follow up request is displayed for a user to explicitly enter alocation identifier, the process may then (box 724) repeat all of someof the search submissions using the entered information (ending at box726). For example, the search may be resubmitted as a search containingexplicit location information, or as coming from a device having a newdefault location. Alternatively, just the local portion of tile searchmay be repeated, and the identification of the relevance of localinformation to the query may, in appropriate circumstances, also berepeated. The display of results may then be updated to show the newlocal results, and potentially to update the positioning of the varioussearch result groups.

FIG. 8 is a block diagram of computing devices 800, 850 that may be usedto implement the systems and methods described in this document, aseither a client or as a server or plurality of servers. Computing device800 is intended to represent various forms of digital computers, such aslaptops, desktops, workstations, personal digital assistants, servers,blade servers, mainframes, and other appropriate computers. Computingdevice 850 is intended to represent various forms of mobile devices,such as personal digital assistants, cellular telephones, smartphones,and other similar computing devices. The components shown here, theirconnections and relationships, and their functions, are meant to beexemplary only, and are not meant to limit implementations describedand/or claimed in this document.

Computing device 800 includes a processor 802, memory 804, a storagedevice 806, a high-speed interface 808 connecting to memory 804 andhigh-speed expansion ports 810, and a low speed interface 8 i 2connecting to low speed bus 814 and storage device 806. Each of thecomponents 802, 804, 806, 808, 810, and 8 i 2, are interconnected usingvarious busses, and may be mounted on a common motherboard or in othermanners as appropriate. The processor 802 can process instructions forexecution within the computing device 800, including instructions storedin the memory 804 or on the storage device 806 to display graphicalinformation for a GUI on an external input/output device, such asdisplay 816 coupled to high speed interface 808. In otherimplementations, multiple processors and/or multiple buses may be used,as appropriate, along with multiple memories and types of memory. Also,multiple computing devices 800 may be connected, with each deviceproviding portions of the necessary operations (e.g., as a server bank,a group of blade servers, or a multi-processor system).

The memory 804 stores information within the computing device 800. Inone implementation, the memory 804 is a computer-readable medium. In oneimplementation, the memory 804 is a volatile memory unit or units. Inanother implementation, the memory 804 is a non-volatile memory unit orunits.

The storage device 806 is capable of providing mass storage for thecomputing device 800. In one implementation, the storage device 806 is acomputer-readable medium. In various different implementations, thestorage device 806 may be a floppy disk device, a hard disk device, anoptical disk device, or a tape device, a flash memory or other similarsolid-state memory device, or an array of devices, including devices ina storage area network or other configurations. In one implementation, acomputer program product is tangibly embodied in an information carrier.The computer program product contains instructions that, when executed,perform one or more methods, such as those described above. Theinformation carrier is a computer- or machine-readable medium, such asthe memory 804, the storage device 806, memory on processor 802, or apropagated signal.

The high-speed controller 808 manages bandwidth-intensive operations forthe computing device 800, while the low speed controller 812 manageslower bandwidth-intensive operations. Such allocation of duties isexemplary only. In one implementation, the high-speed controller 808 iscoupled to memory 804, display 816 (e.g., through a graphics processoror accelerator), and to high-speed expansion ports 810, which may acceptvarious expansion cards (not shown). In the implementation, low-speedcontroller 812 is coupled to storage device 806 and low-speed expansionport 814. The low-speed expansion port, which may include variouscommunication ports (e.g., USB, Bluetooth, Ethernet, wireless Ethernet),may be coupled to one or more input/output devices, such as a keyboard,a pointing device, a scanner, or a networking device such as a switch orrouter, e.g., through a network adapter.

The computing device 800 may be implemented in a number of differentforms, as shown in the figure. For example, it may be implemented as astandard server 820, or multiple times in a group of such servers. Itmay also be implemented as part of a rack server system 824. Inaddition, it may be implemented in a personal computer such as a laptopcomputer 822. Alternatively, components from computing device 800 may becombined with other components in a mobile device (not shown), such asdevice 850. Each of such devices may contain one or more of computingdevice 800, 850, and an entire system may be made up of multiplecomputing devices 800, 850 communicating with each other

Computing device 850 includes a processor 852, memory 864, aninput/output device such as a display 854, a communication interface866, and a transceiver 868, among other components. The device 850 mayalso be provided with a storage device, such as a microdrive or otherdevice, to provide additional storage. Each of the components 850, 852,864, 854, 866, and 868, are interconnected using various buses, andseveral of the components may be mounted on a common motherboard or inother manners as appropriate.

The processor 852 can process instructions for execution within thecomputing device 850, including instructions stored in the memory 864.The processor may also include separate analog and digital processors.The processor may provide, for example, for coordination of the othercomponents of the device 850, such as control of user interfaces,applications run by device 850, and wireless communication by device850.

Processor 852 may communicate with a user through control interface 858and display interface 856 coupled to a display 854. The display 854 maybe, for example, a TFT LCD display or an OLEO display, or otherappropriate display technology. The display interface 856 may compriseappropriate circuitry for driving the display 854 to present graphicaland other information to a user. The control interface 858 may receivecommands from a user and convert them for submission to the processor852. In addition, an external interface 862 may be provided incommunication with processor 852, so as to enable near areacommunication of device 850 with other devices. External interface 862may provide, for example, for wired communication (e.g., via a dockingprocedure) or for wireless communication (e.g., via Bluetooth or othersuch technologies).

The memory 864 stores information within the computing device 850. Inone implementation, the memory 864 is a computer-readable medium. In oneimplementation, the memory 864 is a volatile memory unit or units. Inanother implementation, the memory 864 is a non-volatile memory unit orunits. Expansion memory 874 may also be provided and connected to device850 through expansion interface 872, which may include, for example, aSIMM card interface. Such expansion memory 874 may provide extra storagespace for device 850, or may also store applications or otherinformation for device 850. Specifically, expansion memory 874 mayinclude instructions to carry out or supplement the processes describedabove, and may include secure information also. Thus, for example,expansion memory 874 may be provided as a security module for device850, and may be programmed with instructions that permit secure use ofdevice 850. In addition, secure applications may be provided via theSIMM cards, along with additional information, such as placingidentifying information on the SIMM card in a non-hackable manner.

The memory may include, for example, flash memory and/or NVRAM memory,as discussed below. In one implementation, a computer program product istangibly embodied in an information carrier. The computer programproduct contains instructions that, when executed, perform one or moremethods, such as those described above. The information carrier is acomputer- or machine-readable medium, such as the memory 864, expansionmemory 874, memory on processor 852, or a propagated signal.

Device 850 may communicate wirelessly through communication interface866, which may include digital signal processing circuitry wherenecessary. Communication interface 866 may provide for communicationsunder various modes or protocols, such as GSM voice calls, SMS, EMS, orMMS messaging, CDMA, TOMA, PDC, WCDMA, CDMA2000, or GPRS, among others.Such communication may occur, for example, through radio-frequencytransceiver 868. In addition, short-range communication may occur, suchas using a Bluetooth, WiFi, or other such transceiver (not shown). Inaddition, GPS receiver module 870 may provide additional wireless datato device 850, which may be used as appropriate by applications runningon device 850.

Device 850 may also communicate audibly using audio codec 860, which mayreceive spoken information from a user and convert it to usable digitalinformation. Audio codec 860 may likewise generate audible sound for auser, such as through a speaker, e.g., in a handset of device 850. Suchsound may include sound from voice telephone calls, may include recordedsound (e.g., voice messages, music files, etc.) and may also includesound generated by applications operating on device 850.

The computing device 850 may be implemented in a number of differentforms, as shown in the figure. For example, it may be implemented as acellular telephone 880. It may also be implemented as part of asmartphone 882, personal digital assistant, or other similar mobiledevice.

Various implementations of the systems and techniques described here canbe realized in digital electronic circuitry, integrated circuitry,specially designed ASICs (application specific integrated circuits),computer hardware, firmware, software, and/or combinations thereof.These various implementations can include implementation in one or morecomputer programs that are executable and/or interpretable on aprogrammable system including at least one programmable processor, whichmay be special or general purpose, coupled to receive data andinstructions from, and to transmit data and instructions to, a storagesystem, at least one input device, and at least one output device.

These computer programs (also known as programs, software, softwareapplications or code) include machine instructions for a programmableprocessor, and can be implemented in a high-level procedural and/orobject-oriented programming language, and/or in assembly/machinelanguage. As used herein, the terms “machine-readable medium”“computer-readable medium” refers to any computer program product,apparatus and/or device (e.g., magnetic discs, optical disks, memory,Programmable Logic Devices (PLDs)) used to provide machine instructionsand/or data to a programmable processor, including a machine-readablemedium that receives machine instructions as a machine-readable signal.The term “machine-readable signal” refers to any signal used to providemachine instructions and/or data to a programmable processor.

To provide for interaction with a user, the systems and techniquesdescribed here can be implemented on a computer having a display device(e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor)for displaying information to the user and a keyboard and a pointingdevice (e.g., a mouse or a trackball) by which the user can provideinput to the computer. Other categories of devices can be used toprovide for interaction with a user as well; for example, feedbackprovided to the user can be any form of sensory feedback (e.g., visualfeedback, auditory feedback, or tactile feedback); and input from theuser can be received in any form, including acoustic, speech, or tactileinput.

The systems and techniques described here can be implemented in acomputing system that includes a back-end component (e.g., as a dataserver), or that includes a middleware component (e.g., an applicationserver), or that includes a front-end component (e.g., a client computerhaving a graphical user interface or a Web browser through which a usercan interact with an implementation of the systems and techniquesdescribed here), or any combination of such back-end, middleware, orfront-end components. The components of tile system can beinterconnected by any form or medium of digital data communication(e.g., a communication network). Examples of communication networksinclude a local area network (“LAN”), a wide area network (“WAN”), andthe Internet.

The computing system can include clients and servers. A client andserver are generally remote from each other and typically interactthrough a communication network. The relationship of client and serverarises by virtue of computer programs running on the respectivecomputers and having a client-server relationship to each other.

Embodiments may be implemented, at least in part, in hardware orsoftware or in any combination thereof. Hardware may include, forexample, analog, digital or mixed-signal circuitry, including discretecomponents, integrated circuits (ICs), or application-specific ICs(ASICs). Embodiments may also be implemented, in whole or in part, insoftware or firmware, which may cooperate with hardware. Processors forexecuting instructions may retrieve instructions from a data storagemedium, such as EPROM, EEPROM, NVRAM, ROM, RAM, a CD-ROM, a HOD, and thelike. Computer program products may include storage media that containprogram instructions for implementing embodiments described herein.

A number of implementations have been described. Nevertheless, it willbe understood that various modifications may be made without departingfrom the spirit and scope of this disclosure. Accordingly, otherimplementations are within the scope of the claims.

What is claimed is:
 1. A computer-implemented method, comprising:receiving from a remote device a search query; generating a plurality ofdifferent category-directed result sets for the search query;calculating for each category-directed result set a likelihood valuethat represents a likelihood that the corresponding category-directedresult set is responsive to the received search query; comparing thecalculated likelihood value to a threshold certainty value such that alikelihood value above the threshold certainty value indicates a highcertainty result set; determining an order for the plurality ofcategory-directed result sets based on the search query; andtransmitting, from the computer server system to the remote device, codefor generating a display in a manner so that a summary associated withthe high certainty result set is displayed and the plurality ofcategory-directed result sets are displayed on the remote device in thedetermined order.
 2. The computer-implemented method of claim 1, whereincalculating the likelihood value comprises: retrieving a profile that isassociated with the remote device and that includes a distribution ofpreviously determined correlations between other search queries receivedfrom the remote device and one or more different categories ofinformation; and factoring a portion of the distribution into thecalculated likelihood.
 3. The computer-implemented method of claim 1,wherein calculating the likelihood value comprises retrieving data thatis associated with other search queries received from other remotedevices, the other search queries being substantially similar to thereceived search query.
 4. The computer-implemented method of claim 3,wherein: the data that is associated with other search queries includesa distribution of previously determined correlations between the othersubstantially similar search queries and one or more differentcategories of information; and calculating the likelihood value furthercomprises factoring a portion of the distribution into the calculatedlikelihood.
 5. The computer-implemented method of claim 4, wherein thedistribution includes multiple sub-distributions, each sub-distributionbeing related to any one or more of a classification of device fromwhich the query was received, a model or model group of device fromwhich the query was received, a geographic area from which the query wasreceived, and an approximate time of day at which the query wasreceived.
 6. The computer-implemented method of claim 1, whereincalculating the likelihood value comprises: retrieving a profile that isassociated with the remote device and performing a first calculation toobtain a first result based on a portion of the retrieved profile;retrieving data that is associated with the search query and performinga second calculation to obtain a second result based on a portion of theretrieved data; and performing a third calculation based on a weightedcontribution of the first result and the second result.
 7. The computerimplemented method of claim 1, further comprising determining an orderof display of search results in each of the category-related resultsets.
 8. The computer implemented method of claim 1, wherein thedifferent categories correspond to categories of information selectedfrom a group of location-based results, web results, images, video,shopping, blogs, news, maps, and books.
 9. The computer implementedmethod of claim 1, wherein the order of the plurality ofcategory-related result sets is determined based on a correlationbetween the search query and aggregated prior user activity relating tothe search query or related search queries, and categories for theresult sets.
 10. The computer-implemented method of claim 1, wherein theremote device is a mobile device.
 11. A non-transitory computer-readablemedium storing instructions that, when executed by a processor, causeperformance of operations comprising: generating a plurality ofdifferent category-directed result sets for the search query;calculating for each category-directed result set a likelihood valuethat represents a likelihood that the corresponding category-directedresult set is responsive to the received search query; comparing thecalculated likelihood value to a threshold certainty value such that alikelihood value above the threshold certainty value indicates a highcertainty result set; determining an order for the plurality ofcategory-directed result sets based on the search query; andtransmitting, from the computer server system to the remote device, codefor generating a display in a manner so that a summary associated withthe high certainty result set is displayed and the plurality ofcategory-directed result sets are displayed on the remote device in thedetermined order.
 12. The computer-implemented method of claim 11,wherein calculating the likelihood value comprises: retrieving a profilethat is associated with the remote device and that includes adistribution of previously determined correlations between other searchqueries received from the remote device and one or more differentcategories of information; and factoring a portion of the distributioninto the calculated likelihood.
 13. The computer-implemented method ofclaim 11, wherein calculating the likelihood value comprises retrievingdata that is associated with other search queries received from otherremote devices, the other search queries being substantially similar tothe received search query.
 14. The computer-implemented method of claim13, wherein: the data that is associated with other search queriesincludes a distribution of previously determined correlations betweenthe other substantially similar search queries and one or more differentcategories of information; and calculating the likelihood value furthercomprises factoring a portion of the distribution into the calculatedlikelihood.
 15. The computer-implemented method of claim 14, wherein thedistribution includes multiple sub-distributions, each sub-distributionbeing related to any one or more of a classification of device fromwhich the query was received, a model or model group of device fromwhich the query was received, a geographic area from which the query wasreceived, and an approximate time of day at which the query wasreceived.
 16. The computer-implemented method of claim 11, whereincalculating the likelihood value comprises: retrieving a profile that isassociated with the remote device and performing a first calculation toobtain a first result based on a portion of the retrieved profile;retrieving data that is associated with the search query and performinga second calculation to obtain a second result based on a portion of theretrieved data; and performing a third calculation based on a weightedcontribution of the first result and the second result.
 17. The computerimplemented method of claim 11, further comprising determining an orderof display of search results in each of the category-related resultsets.
 18. A computing device, comprising: a processor; and acomputer-readable medium having instructions stored thereon that, whenexecuted by the processor, cause the processor to perform operationscomprising: generating a plurality of different category-directed resultsets for the search query; generating a plurality of differentcategory-directed result sets for the search query; calculating for eachcategory-directed result set a likelihood value that represents alikelihood that the corresponding category-directed result set isresponsive to the received search query; comparing the calculatedlikelihood value to a threshold certainty value such that a likelihoodvalue above the threshold certainty value indicates a high certaintyresult set; determining an order for the plurality of category-directedresult sets based on the search query; and transmitting, from thecomputer server system to the remote device, code for generating adisplay in a manner so that a summary associated with the high certaintyresult set is displayed and the plurality of category-directed resultsets are displayed on the remote device in the determined order.
 19. Thecomputer-implemented method of claim 18, wherein calculating thelikelihood value comprises: retrieving a profile that is associated withthe remote device and that includes a distribution of previouslydetermined correlations between other search queries received from theremote device and one or more different categories of information; andfactoring a portion of the distribution into the calculated likelihood.20. The computer-implemented method of claim 18, wherein calculating thelikelihood value comprises retrieving data that is associated with othersearch queries received from other remote devices, the other searchqueries being substantially similar to the received search query.